Human Organoid Unsupervised Exploration
Niklas Rindtorff
Loading packages
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## read_xml.response xml2## -- Attaching packages ----------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.1 v purrr 0.3.2
## v tibble 2.1.1 v dplyr 0.8.0.1
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tidyr)
library(here)## here() starts at /Users/rindtorf/github/promiselibrary(ggrastr)## Warning: package 'ggrastr' was built under R version 3.6.3library(cowplot)## Warning: package 'cowplot' was built under R version 3.6.3library(princurve)
library(scico)## Warning: package 'scico' was built under R version 3.6.3library(ggridges)## Warning: package 'ggridges' was built under R version 3.6.3# modeling
library(nnet)loading input data and annotation. Note that on the central cluster, with access to the complete data table, the definition of the input can easily be changed. For remote work, the subsampled dataset “umap_drugs_sampled.Rds” is the default choice.
# I wish I could solve my path problems with the here() package, but experienced unreliable behavior
# PATH = "/dkfz/groups/shared/OE0049/B110-Isilon2/promise/"
PATH = paste0(here::here(), "/")
#umap_df <- read_rds(paste0(PATH, "data/processed/PhenotypeSpectrum/umap_absolute_all_drugs_tidy.Rds"))
umap_df <- read_rds(paste0(PATH, "data/processed/PhenotypeSpectrum/umap_absolute_all_drugs_sampled.Rds"))
organoid_morphology <- read_delim(here::here("references/imaging/visual_classification_organoids.csv"), ";", escape_double = FALSE, trim_ws = TRUE) %>%
dplyr::select(line = organoid, morphology = visual_inspection_v2)## Parsed with column specification:
## cols(
## organoid = col_character(),
## visual_inspection_morphology_2017 = col_character(),
## visual_class_2_2017 = col_double(),
## visual_inspection_v2 = col_character(),
## visual_inspection_size_2017 = col_character(),
## visual_class_1_2017 = col_double(),
## visual_size_ranking_2018 = col_double(),
## visual_cystic_ranking_2018 = col_double(),
## clustering_jan = col_character()
## )Partition inspection
We are able to observe 4 partitions in our data. After manual inspection, it becomes cleat that the two smallest partitions are mostly consisting of
umap_df %>%
ggplot(aes(v1, v2, color = factor(partition))) +
geom_point_rast(alpha = 0.5, size = 0.35) +
scale_color_brewer(type = "qual", palette = "Set2") +
theme_cowplot() +
labs(x = "UMAP 1",
y = "UMAP 2",
color = "partition") +
theme(legend.position = "bottom") +
coord_fixed()
umap_df %>%
dplyr::count(partition) %>%
mutate(ratio = n/sum(n)) %>%
arrange(desc(ratio))## # A tibble: 4 x 3
## partition n ratio
## <fct> <int> <dbl>
## 1 1 283583 0.916
## 2 2 21320 0.0689
## 3 3 3385 0.0109
## 4 4 1228 0.00397I remove 2 partitions from all main figures for ease of reading. Below, it is easy to toggle the removal of partitions on and off to make sure this filtering step is robust
Organoid Size Distributions
I plot a size-distribution.
gg_size_dist <- umap_df %>%
filter(partition %in% c(1,2)) %>%
ggplot(aes(size)) +
geom_histogram() +
theme_cowplot()
gg_size_dist_log <- gg_size_dist +
scale_x_log10()
gg_size_dist_log## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
I add the eCDF.
df <- umap_df %>% filter(partition %in% c(1,2))
gg_ecdf <- ggplot(df %>% filter(drug == "DMSO")) +
stat_ecdf(aes(x = size, group = line),
geom = "step", size = 1) +
#scale_color_manual(values = c("#00AFBB", "#E7B800"))+
labs(y = "f(size)",
x = "organoid size [pixels]") +
theme_cowplot()
gg_ecdf
For more details about distributions, please refer to *reports/Phenotypespectrum/‘xyz’_dist.pdf*.
line_param <- umap_df %>% filter(partition %in% c(1,2)) %>%
nest(-line, -replicate) %>%
mutate(fit = map(data, ~ fitdistrplus::fitdist(.x$size, "lnorm")),
param = map(fit, ~ .x$estimate %>% broom::tidy()))## Warning: 'tidy.numeric' is deprecated.
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## See help("Deprecated")df <- line_param %>% unnest(param) %>%
filter(names == "meanlog") %>%
group_by(line) %>%
mutate(mean_meanlog = mean(x)) %>%
arrange(mean_meanlog) %>%
ungroup() %>%
mutate(line = factor(line, levels = .$line %>% unique()))
organoid_size_factor <- df$line %>% levels()
df <- df %>%
dplyr::select(line, replicate, x) %>%
# tidyr::pivot_wider(names_from = replicate,
# values_from = x)
tidyr::spread(key = replicate, value = x)
r_size = df %>% ungroup() %>% dplyr::select(-line) %>% as.matrix %>% cor() %>% min()
gg_size_replicate <- df %>%
ggplot(aes(`1`, `2`)) +
geom_smooth(method = "lm", se = FALSE, color = "grey") +
geom_point() +
theme_cowplot() +
labs(x = "Replicate 1, mu [ln size]",
y = "Replicate 2, mu [ln size]",
caption = paste0("2 replicates, r= ", round(r_size, 2))) +
coord_fixed(ratio = 1)
#geom_abline(slope = 1, color = "grey")
gg_size_replicate
organoid_size_factor_09 <- umap_df %>% filter(partition %in% c(1,2)) %>% group_by(line) %>%
summarise(x = quantile(size_log, 0.9)) %>%
#summarise(x = mean(size_log)) %>%
arrange(x) %>% .$line
gg_size_dist_morph_ridge <- umap_df %>% filter(partition %in% c(1,2)) %>% filter(drug == "DMSO") %>%
mutate(line = factor(line, levels = organoid_size_factor_09)) %>%
ggplot() +
geom_density_ridges_gradient(aes(y = line, x = size_log, fill = stat(x)), scale = 1) +
#geom_density(aes(x = size_log, group = replicate, color = morphological_class)) +
#facet_wrap(~ line) +
scale_fill_viridis_c() +
labs(caption = "DMSO treated organoids",
x = "ln(size)",
fill = "size") +
theme(legend.position = "bottom") +
theme_cowplot()
gg_size_dist_morph_ridge## Picking joint bandwidth of 0.194
umap_size <- function(umap){
umap %>%
#filter(Size < 1000) %>%
ggplot(aes(v1, v2, color = size_log)) +
geom_point_rast(alpha = 0.5, size = 0.35) +
scale_color_viridis_c() +
theme_cowplot() +
labs(x = "UMAP 1",
y = "UMAP 2",
color = "ln(size)") +
theme(legend.position = "bottom") +
coord_fixed()
}
gg_size <- umap_size(umap_df %>% filter(partition %in% c(1,2)))Organoid Size during Drug Treatment
drug_size <- umap_df %>% filter(partition %in% c(1,2)) %>% filter(drug == "DMSO" | drug == "Paclitaxel") %>%
mutate(concentration = ifelse(drug == "DMSO", 0, concentration)) %>%
#filter(morphological_class == "disorganized") %>%
#filter(morphological_class != "other") %>%
mutate(concentration = factor(concentration, levels = c("0", "0.0016", "0.008", "0.04", "0.2", "1.0")))
ggdrug_size <- ggplot(drug_size) +
geom_density(aes(x = log(size), group = concentration, color = concentration), size = 1.5) +
scico::scale_color_scico_d() +
theme_cowplot() +
#scale_x_continuous(limits = c(0, 15000)) +
theme(legend.position = "bottom") +
labs(color = "Paclitaxel Concentration Factor",
title = "Organoid size distribution",
x = "ln(size)")
ggdrug_size 
drug_count <- drug_size %>%
dplyr::count(concentration, line, replicate, well)
ggdrug_count <- ggplot(drug_count) +
geom_density(aes(x = log(n), group = concentration, color = concentration), size = 1.5) +
scico::scale_color_scico_d() +
theme_cowplot() +
theme(legend.position = "bottom") +
labs(color = "Paclitaxel Concentration Factor",
title = "Organoid count distribution",
x = "ln(n)")
ggdrug_count
set.seed(234)
loi = c("D022T01", "D046T01")
df <- umap_df %>%
filter(partition %in% c(1,2)) %>%
filter(drug == "DMSO" | drug == "Paclitaxel") %>%
mutate(concentration = ifelse(drug == "DMSO", 0, concentration)) %>%
filter(line == loi)## Warning in line == loi: longer object length is not a multiple of shorter
## object lengthgg_drug <- umap_df %>% filter(partition %in% c(1,2)) %>%
dplyr::select(-line, -concentration) %>%
ggplot(aes(v1, v2)) +
geom_point_rast(alpha = 1, size = 0.35, color = "#f1f1f1") +
geom_point_rast(data = df %>%
group_by(concentration) %>%
sample_n(1000, replace = TRUE),
aes(color = concentration),alpha = 1, size = 1.5, shape=16) +
#facet_wrap( ~ concentration, ncol = 1) +
#scale_color_brewer(type = "seq", palette = "YlOrRd") +
#geom_density2d(color = "black") +
theme_classic() +
labs(x = "UMAP 1",
y = "UMAP 2",
caption = paste0(paste(loi, collapse=" "), ", Paclitaxel"),
color = "Concentration Factor") +
scico::scale_color_scico_d() +
facet_wrap(~ line, ncol = 2) +
theme(legend.position = "bottom") +
#theme_cowplot(font_size = 8) +
theme(legend.position = "bottom")
gg_drug
loi <- c("D022T01", "D055T01")
drug_size_param <- drug_size %>%
nest(-concentration, -line) %>%
mutate(fit = map(data, ~ fitdistrplus::fitdist(.x$size, "lnorm")),
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## See help("Deprecated")df <- drug_size_param %>% unnest(param) %>%
filter(names == "meanlog") %>%
mutate(concentration = factor(concentration, levels = c("0", "0.0016", "0.008", "0.04", "0.2", "1.0")))
gg_size_drug <- df %>%
filter(line %in% loi) %>%
#mutate(concentration = as.numeric(as.character(concentration))) %>%
ggplot(aes(concentration, x)) +
geom_point(color = "grey") +
geom_line(data = df %>% dplyr::rename(line_h = line) , aes(group = line_h), color = "grey") +
geom_point(data = df %>% dplyr::rename(line_h = line), color = "grey") +
geom_point(color = "black") +
geom_line(aes(group = line), color = "black") +
labs(y = 'mu ln(size)' ,
x = "Concentration Factor",
caption = paste0(paste(loi, collapse=" "), ", Paclitaxel")) +
facet_wrap(~ line)+
theme_cowplot()
gg_size_drug
Organoid Heterogeneity
I plot 2 organoid lines treated with DMSO control
set.seed(234)
df <- umap_df %>% filter(partition %in% c(1,2)) %>%
mutate(cystic = if_else(line == "D013T01" & well == "D24" & plate == "D013T01P001L02", TRUE, FALSE)) %>%
mutate(compact = if_else(line == "D055T01" & well == "D24" & plate == "D055T01P007L02", TRUE, FALSE))
gg_cys_comp <- df %>%
sample_frac(0.01) %>%
ggplot(aes(v1, v2, color = size_log)) +
#scale_color_brewer(type = "qual", palette = 2) +
geom_point_rast(alpha = 0.1, size = 0.35) +
geom_point_rast(color = "#F4B400", alpha = 1, size = 0.5, data = df %>% filter(cystic == TRUE)) +
geom_point_rast(color = "#DB4437", alpha = 1, size = 0.5, data = df %>% filter(compact == TRUE)) +
scale_color_viridis_c() +
labs(color = "size",
caption = "yellow: D013T01, red: D055T01",
x = "UMAP 1",
y = "UMAP 2") +
theme_cowplot() +
coord_fixed()
gg_cys_comp
In general, DMSO treated organoid lines cover the same latent space than drug treated organoids. This is likely influenced by the large number of untreated organoids in the dataset.
set.seed(123)
df <- umap_df %>% filter(partition %in% c(1,2))
gg_size_supp <- df %>%
mutate(drug = if_else(drug == "DMSO", "DMSO", "other")) %>%
ggplot(aes(v1, v2, color = size_log)) +
geom_point_rast(alpha = 0.5, size = 0.35) +
scale_color_viridis_c() +
theme_cowplot() +
labs(x = "UMAP 1",
y = "UMAP 2") +
theme(legend.position = "bottom") +
facet_wrap(~ drug)
gg_size_supp + ggsave(paste0(PATH, "reports/figures/gg_size_all.pdf"))## Saving 7 x 5 in image
Organoid line differences
I create a single plot showing the two extreme organoid lines and their distribution within the embedding.
set.seed(123)
loi <- c("D055T01", "D007T01", "D027T01", "D018T01") #c("D055T01", "D007T01", "D021T01", "D019T01", "D027T01")
#loi <- umap_df$line %>% unique()
df <- umap_df %>%
filter(drug == "DMSO") %>%
filter(partition %in% c(1,2))
gg_line <- df %>% dplyr::select(-line) %>%
ggplot(aes(v1, v2)) +
geom_point_rast(alpha = 1, size = 0.35, color = "#f1f1f1") +
geom_point_rast(data = umap_df %>%
filter(drug == "DMSO") %>%
# filter(line %in% c("D021T01")) %>%
filter(line %in% loi) %>%
mutate(line = factor(line, levels = loi)) %>%
sample_frac(0.1),
#mutate(line = factor(line, levels = c("D021T01"))),
aes(color = line),alpha = .4, size = 0.35, shape=16) +
facet_wrap( ~ line, ncol =2) +
scale_color_brewer(type = "qual", palette = "Set2") +
#scale_color_manual(values = c(c("#D80D12", "#461C01", "#9a4c91", "#70BE6F", "#24345E"))) +
#geom_density2d(color = "black") +
theme_classic() +
labs(x = "UMAP 1",
y = "UMAP 2")+
#caption = "control treated organoids") +
theme_cowplot(font_size = 8) +
theme(legend.position = "nothing")
gg_line + ggsave(paste0(PATH, "reports/figures/gg_size_all.pdf"), width = 4, height = 4)
I am focusing on cystic vs solid organoid lines
#UMAP Cystic (Lines 18, 13, 27, 30) vs. Solid (others) treated with DMSO, for Figure 1 / matching expression analysis done for cystic vs. rest
set.seed(123)
# TODO reference morphology from
cystic_l <- organoid_morphology %>% filter(morphology == "cystic") %>%.$line %>% paste0(., "01")
dense_l <- organoid_morphology %>% filter(morphology == "solid") %>%.$line %>% paste0(., "01")
df <- umap_df %>%
filter(drug == "DMSO") %>%
filter(partition %in% c(1,2)) %>%
mutate(morphology = case_when(line %in% cystic_l ~ "cystic",
line %in% dense_l ~ "dense",
TRUE ~ "other"))
gg_cystic <- umap_df %>%
ggplot(aes(v1, v2)) +
geom_point_rast(alpha = 1, size = 0.35, color = "#f1f1f1") +
# geom_point_rast(data = df %>%
# filter(morphology == "cystic") %>%
# sample_frac(0.05),
# aes(color = morphology),alpha = .4, size = 0.35, shape=16) +
geom_density_2d(data = df %>%
filter(morphology != "other") %>%
sample_frac(0.05),
aes(color = morphology), size = 1.5) +
scale_color_brewer(type = "qual", palette = "Set2") +
#scale_color_manual(values = c(c("#D80D12", "#461C01", "#9a4c91", "#70BE6F", "#24345E"))) +
#geom_density2d(color = "black") +
theme_classic() +
labs(x = "UMAP 1",
y = "UMAP 2")+
#caption = "control treated organoids") +
theme_cowplot(font_size = 8) +
#theme(legend.position = "nothing") +
coord_fixed()
gg_cystic
Plot Export
#gg_size_dist + ggsave(paste0(PATH, "reports/figures/gg_size_dist.pdf"))
#gg_size_dist_log + ggsave(paste0(PATH, "reports/figures/gg_size_dist_log.pdf"))
#ggdrug_size + ggsave(paste0(PATH, "reports/figures/gg_drug_dist_size_log.pdf"))
#gg_ecdf + ggsave(paste0(PATH, "reports/figures/gg_size_dist_ecdf.pdf"))
#gg_size_dist_morph_ridge + ggsave(paste0(PATH, "reports/figures/gg_size_dist_morph_ridge.pdf"), width = 4, height = 4)
# ggdrug_count + ggsave(paste0(PATH, "reports/figures/gg_drug_dist_n_log.pdf"))
# gg_drug + ggsave(paste0(PATH, "reports/figures/gg_drug.pdf"), width = 8, height = 4)
# gg_size_drug + ggsave(paste0(PATH, "reports/figures/gg_trametinib_size_dose.pdf"), width = 3.65, height = 3.65)
gg_cystic + ggsave(paste0(PATH, "reports/figures/gg_cystic.pdf"), width = 4, height = 4)
plot_grid(plot_grid(gg_size_dist_morph_ridge, gg_size_replicate, labels = c('A', 'B'), label_size = 12, ncol = 2),
gg_size,
plot_grid(gg_line, gg_cystic, labels = c('D', 'E'), label_size = 12, ncol = 2),
labels = c('', 'C', ''), label_size = 12, ncol = 1) +
ggsave(paste0(PATH, "reports/panels/panel_size_dist.pdf"), width = 8, height = 16)
plot_grid(plot_grid(ggdrug_size, ggdrug_count, labels = c('A', 'B'), label_size = 12),
gg_drug,
gg_size_drug,
labels = c('', 'C', 'D'), label_size = 12, ncol = 1) +
ggsave(paste0(PATH, "reports/panels/panel_size_drug.pdf"), width = 8, height = 12)
gg_size_suppSupplement
Here I collect pieces of code that did not make it into the final analysis but can be run in theory. In order to access these peaces of code, you have to open the .RMD file.
knitr::knit_exit()